PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
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Summary
This paper describes the challenges of computation on natural graphs in the context of existing graph-parallel abstractions and introduces the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges.
- Type
- article
- Published
- 2012-10-08
- Cited by
- 2,026
- References
- 53
- OpenAlex
- https://openalex.org/W78077100
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:13396177
Keywords
Computer science, Exploit, Scalability, Theoretical computer science, Graph
References
- Large-scale Graph Computation on Just a PC
- GraphChi: Large-Scale Graph Computation on Just a PC
- Residual Splash for Optimally Parallelizing Belief Propagation
- Streaming graph partitioning for large distributed graphs
- Twister: a runtime for iterative MapReduce
- On power-law relationships of the Internet topology
- The drinking philosophers problem
- The webgraph framework I: compression techniques
- Parallel Multilevel k-way Partitioning Scheme for Irregular Graphs
- On compressing social networks
- An architecture for parallel topic models
- LFGraph: simple and fast distributed graph analytics
- Error and attack tolerance of complex networks
- Layered label propagation: a multiresolution coordinate-free ordering for compressing social networks
- Book Review : Parallel and Distributed Computation: Numerical Methods
- Hierarchical ordering of sequential processes
- Distributed GraphLab: A Framework for Machine Learning in the Cloud
- What is Twitter, a social network or a news media?
- Graph evolution: Densification and shrinking diameters
- Counting triangles and the curse of the last reducer
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- Doubly separable models and distributed parameter estimation
- KOALA-C: A Scheduler for Integrated Multi-Cluster and Multi-Cloud Environments
- From "Think Like a Vertex" to "Think Like a Graph"
- Large-scale Graph Computation on Just a PC
- An Xdata Architecture for Federated Graph Models and Multi-tier Asymmetric Computing
- Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning
- M-Flash: Fast Billion-scale Graph Computation Using Block Partition Model
- Massive-scale processing of record-oriented and graph data
- DREAM: Dresden Streaming Transactional Memory Benchmark
- Algorithmic building blocks for relationship analysis over large graphs
- New Trends in Databases and Information Systems: ADBIS 2020 Short Papers, Lyon, France, August 25–27, 2020, Proceedings
- ImmortalGraph: A System for Storage and Analysis of Temporal Graphs
- Fast, exact graph diameter computation with vertex programming
- Paradigms for Realizing Machine Learning Algorithms
- Scalability! But at what COST?
- Optimizing Network Performance in Distributed Machine Learning
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